How Logistics Can Transform With AI — and Not Lose Its Way

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The buzz around artificial intelligence has a way of obscuring the most important conversations around it. If you’re like me, your inbox and feeds are oversaturated with AI-related content. Most of this has told you how it’s “going to change everything,” and probably very little has told you how.

You may also feel a sense of AI vertigo. With so many takes, written from so many different perspectives, each trying to find their niche, it is a major challenge for anybody to meaningfully know up from down.

But logistics professionals cannot afford to simply tune out. The opportunities and risks around AI within our industry are simply too profound. With this in mind, let’s look beyond the headlines and consider a more practical, bottom-line approach.

AI — specifically generative AI — will be supremely helpful in building more resilient supply chains. But it’s not magic. GAI just happens to be an excellent partner for the specific things we do in logistics.

Here is a confession: I’ve always found the words “supply chain” to be a bit misleading. A chain suggests something linear, with one identical piece following another down a line. As anybody who’s spent more than a day in this industry can tell you, logistics is anything but linear. It depends upon swirling sets of interrelated variables, all coming together to produce, for our customers, an experience of seamless convenience.

What’s behind this illusion are scores of brilliant minds, robust processes and powerful technologies coming together to solve hundreds of thousands of complex problems every day.

There is no doubt a significant amount of planning, perseverance and grit is behind all this. But what this industry produces most of all is data, or information. Coordinating all this complexity isn’t a matter of magic or luck. It depends on data, and more gets generated every day, from routes chosen to less-than-truckload pricing, fuel costs, delivery schedules, warehouse inventories and more.

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Singh 

Pick any variable within our industry. Terabytes of data are generated on it every day. Coordinating all this data and using it to optimize our processes is beyond the ability of any human mind, or even teams of human minds. We do our best, and we do an excellent job, but if we’re being honest with ourselves, it’s essentially a piecemeal process relative to the amount of information we’re trying to harness.

With GAI, information could be at your fingertips — including information that is in a constant state of change, such as weather conditions, port congestion, labor availability, regulations and even market trends.

For instance, let’s consider a global logistics provider managing the movement of goods. GAI can quickly process information across a number of variables to create well-optimized route plans.

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To take it a step further, these plans can also consider aspects such as carbon footprint, transit modes and costs, all while ensuring the efficient and safe delivery of goods. And the more data and human prompts, the better the provider will get at this.

Now zoom out and apply this same model to every key process in your operations. Your business just entered a new league in productivity.

It’s clear to me that we’re on a precipice of revolution that we haven’t seen since, well, the Industrial Revolution. The comparison is apt. The Industrial Revolution unlocked untold levels of productivity and well-­being. It also ushered in new profound ethical problems around labor and spiked seismic climate change. This new AI revolution will have its own problem sets, and logistics will have to be prepared to meet the iterations of those problems specific to our industry.

Obviously, if GAI feeds on data, we’ll need to ensure that, as we feed these models, data and information that is private and proprietary to our businesses is not leaked. This will go beyond awkward uses of existing GAI models like ChatGPT to more bespoke use cases. It will also mean some kind of regulatory infrastructure emerging to address and standardize privacy concerns and infractions.

Labor also has its own problems with GAI. We need to think now about how to transition our workforce to work with GAI. New technologies may result in job losses, but they also create new jobs. We should be thinking through this time at a macro level in terms of a “shock” that logistics companies will need to weather appropriately, taking advantage of new technologies while doing right by their employees.

A key component to successfully embracing this tech advancement will be in how we respond as individuals. We may be too mired in our ways of doing things, or we may be too fearful of change.

And while it may be easiest to turn a blind eye hoping AI will go away, the opportunities are too great to react with complacency. To leverage the opportunities and be a part of responsible AI integration, we must educate ourselves and those we work with to help adopt and adapt.

Dealing with these chal­lenges, and others, presents a unique cooperative opportunity for the logistics industry.

Governance shouldn’t be tackled case by case, company by company. Logistics leaders need to begin thinking through these potential problems to­gether, building governance frameworks around privacy, labor and other issues that will prepare the industry for the shifts we’re about to experience.

This kind of cooperation may seem foreign to those of us who are locked in a competitive mindset, but GAI’s risks are simply too large to manage via course-correction and learning by example.

For instance, the push for the digital bill of lading in the trucking industry has resulted in a group of parties, spear­headed by the Digital LTL Council, putting aside their competitive interests to push for a framework that would make the industry do better.

Problem-solving around GAI will require an even greater expression of cooperation globally, and it is possible.

GAI’s potential in our industry is huge and will keep maturing. To ensure these opportunities are safe, we need to build out a global governance response that considers the various counter­weighing risks and challenges that GAI poses.

Maneet Singh is chief information officer at multimodal logistics firm Odyssey Logistics & Technology. He is responsible for the company's technology and cybersecurity strategy, global IT operations and IT transformation initiatives. Odyssey Logistics ranks No. 49 on the Transport Topics Top 100 list of the largest logistics companies in North America.